Naive Bayes Classification

import pandas as pd
import numpy as np

data = pd.read_csv('./data/Customer_Behaviour.csv')
data.head(400)

	User ID	Gender	Age	EstimatedSalary	Purchased
0	15624510	Male	19	19000	0
1	15810944	Male	35	20000	0
2	15668575	Female	26	43000	0
3	15603246	Female	27	57000	0
4	15804002	Male	19	76000	0
...	...	...	...	...	...
395	15691863	Female	46	41000	1
396	15706071	Male	51	23000	1
397	15654296	Female	50	20000	1
398	15755018	Male	36	33000	0
399	15594041	Female	49	36000	1

400 rows × 5 columns

Gender 0 - Female 1 - Male

from sklearn import preprocessing

label_encoder = preprocessing.LabelEncoder()

gender_encoded = label_encoder.fit_transform(data["Gender"])
age_encoded = label_encoder.fit_transform(data["Age"])
income_encoded = label_encoder.fit_transform(data["EstimatedSalary"])
purchase_encoded = label_encoder.fit_transform(data["Purchased"])

features = list(zip(gender_encoded, age_encoded, income_encoded))
print(features)

[(1, 1, 4), (1, 17, 5), (0, 8, 26), (0, 9, 39), (1, 1, 57), (1, 9, 40), (0, 9, 65), (0, 14, 116), (1, 7, 17), (0, 17, 47), (0, 8, 61), (0, 8, 35), (1, 2, 67), (1, 14, 3), (1, 0, 63), (1, 11, 61), (1, 29, 9), (1, 27, 10), (1, 28, 12), (0, 30, 13), (1, 27, 7), (0, 29, 32), (1, 30, 24), (0, 27, 7), (1, 28, 8), (1, 29, 5), (1, 31, 12), (0, 29, 14), (1, 11, 26), (1, 13, 3), (1, 13, 55), (0, 9, 105), (0, 3, 1), (0, 10, 27), (1, 9, 71), (1, 17, 11), (0, 15, 12), (1, 12, 32), (0, 8, 53), (0, 9, 15), (0, 9, 2), (0, 15, 34), (1, 17, 85), (1, 12, 0), (0, 10, 65), (1, 5, 5), (1, 7, 60), (0, 9, 37), (1, 12, 104), (0, 13, 70), (0, 6, 16), (0, 0, 27), (0, 11, 64), (0, 17, 8), (0, 9, 40), (0, 6, 38), (0, 5, 31), (1, 10, 60), (1, 4, 3), (0, 14, 91), (1, 9, 5), (1, 7, 68), (0, 5, 48), (1, 14, 94), (0, 41, 64), (1, 6, 40), (1, 6, 4), (0, 5, 63), (0, 4, 45), (0, 13, 49), (1, 7, 61), (0, 6, 11), (0, 2, 8), (0, 15, 87), (1, 14, 3), (1, 16, 86), (1, 0, 35), (0, 4, 11), (0, 10, 68), (0, 8, 2), (1, 12, 61), (1, 21, 25), (1, 2, 32), (1, 17, 69), (0, 12, 44), (0, 13, 92), (1, 6, 38), (0, 10, 66), (1, 8, 62), (1, 17, 33), (1, 4, 62), (0, 12, 90), (1, 8, 0), (0, 11, 12), (0, 11, 64), (0, 17, 27), (0, 17, 9), (1, 10, 96), (1, 17, 54), (0, 10, 21), (1, 9, 69), (1, 10, 41), (0, 14, 67), (0, 15, 115), (0, 1, 6), (1, 3, 53), (0, 8, 19), (1, 9, 70), (1, 8, 67), (0, 20, 61), (0, 21, 52), (0, 19, 52), (1, 20, 43), (1, 19, 38), (1, 24, 61), (1, 22, 39), (1, 17, 56), (1, 18, 35), (1, 22, 41), (1, 23, 41), (0, 18, 56), (1, 19, 53), (0, 22, 56), (1, 17, 36), (0, 23, 34), (0, 21, 43), (1, 24, 47), (1, 8, 16), (1, 12, 2), (0, 8, 65), (1, 13, 40), (1, 15, 15), (1, 12, 68), (0, 3, 49), (0, 10, 38), (1, 5, 45), (0, 2, 63), (1, 12, 84), (0, 10, 41), (1, 1, 9), (1, 1, 66), (0, 0, 49), (1, 17, 41), (1, 12, 70), (0, 16, 9), (0, 6, 70), (0, 9, 75), (0, 23, 14), (1, 11, 43), (1, 2, 55), (0, 8, 0), (1, 23, 28), (1, 13, 57), (0, 18, 33), (1, 22, 30), (0, 13, 0), (1, 28, 41), (1, 11, 56), (1, 8, 14), (0, 14, 104), (1, 14, 78), (1, 7, 71), (0, 19, 17), (1, 17, 22), (0, 15, 50), (0, 0, 67), (0, 4, 38), (0, 17, 52), (1, 11, 114), (0, 11, 30), (1, 3, 69), (1, 16, 89), (0, 8, 92), (0, 16, 26), (0, 16, 53), (0, 5, 12), (0, 17, 30), (1, 7, 7), (1, 6, 8), (0, 13, 18), (1, 8, 1), (0, 13, 52), (0, 14, 91), (1, 15, 26), (0, 15, 42), (1, 13, 48), (0, 2, 63), (0, 15, 24), (1, 17, 53), (1, 10, 16), (1, 6, 65), (0, 1, 10), (1, 11, 26), (1, 1, 51), (1, 10, 70), (1, 16, 26), (0, 12, 60), (0, 2, 20), (1, 8, 61), (1, 17, 7), (1, 17, 23), (1, 31, 55), (0, 21, 103), (0, 23, 52), (0, 40, 79), (0, 29, 30), (0, 37, 100), (0, 34, 88), (0, 22, 109), (0, 28, 7), (0, 30, 75), (1, 34, 116), (0, 41, 25), (1, 17, 40), (1, 29, 26), (0, 42, 85), (1, 31, 47), (1, 22, 59), (0, 28, 75), (1, 41, 110), (0, 23, 61), (1, 17, 72), (1, 19, 111), (1, 42, 80), (0, 17, 42), (1, 19, 36), (0, 18, 98), (1, 38, 102), (0, 22, 53), (0, 24, 61), (0, 17, 113), (1, 21, 25), (1, 22, 84), (1, 31, 67), (0, 20, 86), (1, 28, 60), (1, 22, 39), (0, 19, 61), (0, 28, 63), (0, 35, 110), (1, 24, 115), (1, 20, 41), (0, 32, 69), (0, 38, 81), (0, 23, 53), (0, 33, 112), (0, 17, 33), (0, 39, 95), (1, 23, 35), (0, 17, 76), (0, 26, 23), (1, 19, 35), (0, 30, 103), (0, 19, 112), (0, 32, 27), (0, 34, 71), (0, 23, 53), (1, 22, 39), (0, 40, 74), (0, 27, 101), (0, 17, 58), (1, 18, 111), (0, 37, 97), (0, 17, 53), (1, 30, 71), (0, 24, 85), (1, 22, 56), (1, 19, 55), (0, 29, 111), (1, 22, 43), (0, 25, 102), (0, 41, 57), (1, 42, 25), (1, 21, 83), (0, 39, 10), (1, 39, 55), (1, 20, 52), (1, 31, 69), (0, 34, 22), (0, 32, 20), (0, 41, 69), (1, 17, 43), (1, 19, 51), (0, 34, 6), (1, 30, 108), (0, 19, 73), (0, 19, 44), (0, 30, 106), (1, 23, 60), (0, 19, 59), (1, 21, 103), (1, 31, 70), (1, 37, 23), (1, 19, 58), (0, 17, 39), (0, 18, 45), (1, 24, 54), (0, 25, 86), (1, 27, 60), (1, 28, 91), (0, 40, 22), (1, 30, 55), (0, 19, 105), (1, 19, 60), (0, 22, 42), (1, 24, 37), (0, 33, 103), (0, 29, 87), (1, 18, 97), (0, 20, 33), (0, 24, 51), (1, 21, 75), (0, 20, 33), (0, 31, 108), (0, 21, 60), (0, 21, 56), (0, 36, 81), (1, 17, 38), (1, 27, 16), (1, 18, 42), (0, 34, 106), (0, 35, 63), (1, 23, 35), (0, 30, 14), (0, 30, 101), (0, 23, 42), (1, 23, 53), (0, 24, 56), (1, 18, 92), (0, 29, 84), (1, 20, 34), (0, 30, 93), (1, 24, 47), (1, 22, 47), (1, 39, 42), (0, 18, 37), (1, 40, 111), (1, 17, 60), (0, 20, 38), (1, 21, 95), (0, 35, 81), (1, 17, 56), (0, 20, 47), (0, 29, 34), (1, 29, 82), (0, 23, 45), (1, 35, 53), (0, 36, 85), (1, 21, 58), (1, 20, 43), (0, 20, 87), (1, 19, 56), (0, 24, 71), (0, 19, 39), (1, 18, 77), (1, 42, 18), (1, 36, 51), (0, 23, 53), (1, 22, 52), (1, 24, 37), (1, 25, 99), (0, 35, 18), (0, 29, 33), (0, 24, 60), (1, 24, 81), (0, 41, 13), (0, 40, 30), (1, 28, 69), (1, 20, 52), (0, 36, 10), (0, 42, 29), (1, 42, 64), (0, 21, 54), (1, 41, 100), (0, 19, 61), (0, 28, 16), (0, 28, 55), (0, 24, 36), (1, 23, 68), (0, 40, 8), (1, 24, 46), (1, 30, 17), (0, 26, 107), (1, 31, 12), (0, 39, 17), (1, 38, 42), (0, 31, 23), (1, 21, 52), (1, 29, 18), (0, 30, 19), (1, 30, 17), (1, 29, 8), (0, 27, 28), (1, 42, 25), (0, 21, 41), (0, 28, 24), (1, 33, 8), (0, 32, 5), (1, 18, 17), (0, 31, 20)]

x = features
y = purchase_encoded

from sklearn.model_selection import train_test_split 
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=1)

Purchase 0 - No 1 - Yes

from sklearn.naive_bayes import GaussianNB

model = GaussianNB()

model.fit(x_train, y_train)

purchase_predicted = model.predict(x_test)

from sklearn import metrics

print("Model accuracy(in %):", metrics.accuracy_score(y_test, purchase_predicted)*100)

Model accuracy(in %): 87.5

print(x_test)

[(1, 18, 17), (0, 21, 43), (1, 18, 92), (1, 21, 95), (0, 8, 92), (0, 20, 47), (0, 2, 20), (1, 31, 70), (1, 13, 3), (1, 30, 108), (0, 16, 53), (0, 21, 54), (1, 17, 53), (0, 30, 101), (0, 35, 63), (1, 38, 102), (1, 42, 64), (1, 9, 40), (0, 10, 68), (1, 42, 80), (0, 22, 56), (0, 32, 69), (0, 26, 107), (1, 29, 26), (1, 27, 10), (1, 8, 0), (0, 40, 30), (1, 31, 55), (0, 35, 18), (0, 34, 88), (1, 21, 25), (1, 1, 57), (0, 0, 67), (1, 39, 55), (0, 9, 65), (1, 12, 61), (1, 4, 3), (0, 14, 67), (0, 32, 5), (1, 1, 9)]

print(purchase_predicted)

[0 0 1 1 0 0 0 1 0 1 0 0 0 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 0 0
 0 1 0]

test = model.predict([(1, 40, 92)])
print("Purchased: {}".format(test))

Purchased: [1]

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